When observation of a signal is performed in an environment where reflections, reverberations, and so on exist, the signal is observed as a convolved version of a clean signal with reflections, reverberations, and so on. Hereafter, the clean signal will be referred to as an “original signal”, and the signal that is observed will be referred to as an “observed signal”. In addition, the distortion convolved on the original signal such as reflections, reverberations, and so on will be referred to as “transfer characteristics”. Accordingly, it is difficult to extract the characteristics inherent in the original signal from the observed signal. Conventionally, various techniques of signal distortion elimination have been devised to resolve this inconvenience. Signal distortion elimination is a processing for eliminating transfer characteristics convolved on an original signal from an observed signal.
A signal distortion elimination processing disclosed in Non-patent literature 1 will now be described as an example of conventional signal distortion elimination methods with reference to FIG. 15. A prediction error filter calculation unit (901) performs frame segmentation on an observed signal, and performs linear prediction analysis on the observed signals included in the respective frames in order to calculate prediction error filters. In the present specification, a filter refers to a digital filter, and calculating so-called filter coefficients that operate on samples of a signal may be simply expressed as “calculating a filter”. A prediction error filter application unit (902) applies the above-described prediction error filter calculated for each frame to the observed signal of the corresponding frame. An inverse filter calculation unit (903) calculates an inverse filter that maximizes the normalized kurtosis of the signal obtained by applying the inverse filter to the prediction error filter-applied signal. An inverse filter application unit (904) obtains a distortion-reduced signal (restored signal) by applying the above-described calculated inverse filter to the observed signal.    Non-patent literature 1: B. W. Gillespie, H. S. Malvar and D. A. F. Florencio, “Speech dereverberation via maximum-kurtosis subband adaptive filtering,” IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 3701-3704, 2001.